Thinking cap: combining personalized, model-driven, and adaptive high definition trans-cranial stimulation (HD-tCS) with functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) brain state measurement and feedback

ABSTRACT

Described is system for mapping user behavior to brain regions of interest. Using a cognitive-behavior model, a behavioral task is selected that is suited for a desired brain state. Using a functional-anatomical model coupled to the cognitive-behavior model, a set of high-definition neurostimulations is selected to be applied to the user during performance of the selected behavioral task. The selected set of high-definition neurostimulations targets specific regions of the user&#39;s brain. Changes in the user&#39;s brain state are sensed during application of the set of high-definition neurostimulations and performance of the selected behavioral task using at least one brain monitoring technique. The coupled functional-anatomical and cognitive-behavior models are adapted until the desired brain state is reached.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a Divisional Application of U.S. application Ser. No.14/987,467, now U.S. Pat. No. 10,071,245, filed in the United States onJan. 4, 2016, entitled, “The Thinking Cap: Combining Personalized,Model-Driven, and Adaptive HD-tCS with fNIRs and EEG Brain StateMeasurement and Feedback,” which is a Non-Provisional Application ofU.S. Provisional Application No. 62/099,835, filed in the United Stateson Jan. 5, 2015, entitled, “The Thinking Cap: Combining Personalized,Model-Driven, and Adaptive HD-tCS with fNIRs and EEG Brain StateMeasurement and Feedback,” which are incorporated herein by reference intheir entirety.

BACKGROUND OF INVENTION (1) Field of Invention

The present invention relates to a system for mapping user behavior tobrain regions of interest and, more particularly, to a system formapping user behavior to brain regions of interest using a combinationof cognitive-behavioral and functional-anatomical modeling.

(2) Description of Related Art

Neurostimulation has been recently developed as a viable tool for:cognitive training and enhancement, rapid recovery from brain injuryincluding stroke, traumatic-brain-injuries, and as a teaching andlearning assistance tool. However, while a number of experiments havedemonstrated performance enhancement due to various forms ofneurostimulation interventions, most studies show high variability and atendency for some users to do worse even though the overall performanceof the user pool improves (see the List of Incorporated LiteratureReferences, Literature Reference No. 47).

Current methods of cognitive enhancing neurostimulation have beenlimited by task specific improvements, a lack of personalization andadaptation, and a limited understanding of mechanistic changes. Thesemethods have shown only limited applicability and transition potentialto working environments.

Other methods use anatomical models, such as those described inLiterature Reference Nos. 2 and 3, to direct neurostimulation, butcannot make predictions of human or animal cognitive behaviors based onneurobiological mechanisms through lesion studies or neurotransmitterimbalances (see Literature Reference No. 4).

Thus, a continuing need exists for a system that will personalize andadapt neurostimulations to pinpoint the phenotypic neurobiologicalmechanisms across a large population with a variety of neural imagingmethods.

SUMMARY OF THE INVENTION

The present invention relates to a system for mapping user behavior tobrain regions of interest and, more particularly, to a system formapping user behavior to brain regions of interest using a combinationof cognitive-behavioral and functional-anatomical modeling. The systemcomprises one or more processors and a memory having instructions suchthat when the instructions are executed, the one or more processorsperform multiple operations. Using a functional-anatomical model coupledto a cognitive-behavior model, a set of high-definitionneurostimulations is selected, wherein the selected set ofhigh-definition neurostimulations targets specific regions of the user'sbrain. Conditions in the user's brain state are sensed duringapplication of the set of high-definition neurostimulations andperformance of a selected behavioral task using at least one brainmonitoring technique. The coupled functional-anatomical andcognitive-behavior models are adapted until the desired brain state isreached.

In another aspect, a set of behavioral performance deficiencies in theuser is assessed. The set of behavioral performance deficiencies areassociated with brain states in various brain regions of the user. Theuser is analyzed with a neuroimaging device as the user performs aplurality of behavioral tasks, wherein the user's performance is used toparameterize the cognitive-behavior model. The cognitive-behavior modelis implemented in a cognitive simulator. The cognitive-behavior model isused to predict the user's performance for a plurality of relatedbehavioral tasks. The cognitive-behavior model is used to generate theset of behavioral tasks. For each task, the set of behavioral tasks issearched for the desired brain state for the user.

In another aspect, the functional-anatomical model is used to associatebrain regions of the user for the desired brain state to specificphysical locations within the skull of the user. Thefunctional-anatomical model is used to select the set of high-definitionneurostimulations to be applied to reach the associated brain regions ofthe user effectively to induce the desired brain state.

In another aspect, the cognitive-behavior model is used to assesschanges in the user's brain state as the user performs the selectedbehavioral task. A new behavioral task in the set of behavioral tasks isoutput for the user to perform.

In another aspect, the cognitive-behavior model is used to identifyspecific regions of the user's brain to be targeted with a selected setof high-definition neurostimulations during performance of the newbehavioral task based on a previous brain state of the user.

In another aspect, two brain monitoring techniques are used to sensechanged in the user's brain state, wherein the first brain monitoringtechnique is electoencephalography (EEG) to monitor brain activity in ananterior cingulate region of the user's brain, and wherein the secondbrain monitoring technique is functional near-infrared spectroscopy(fNIRS) to monitor brain activity in a prefrontal cortex region of theuser's brain.

In another aspect, the system selects using a cognitive-behavior model,a behavioral task from a set of behavioral tasks that is suited for adesired brain state.

In another aspect, the system applies the set of high-definitionneurostimulations to the user during performance of the selectedbehavioral task.

In another aspect, the present invention also comprises a method forcausing a processor to perform the operations described herein andperforming the listed operations.

Finally, in yet another aspect, the present invention also comprises acomputer program product comprising computer-readable instructionsstored on a non-transitory computer-readable medium that are executableby a computer having a processor for causing the processor to performthe operations described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features and advantages of the present invention will beapparent from the following detailed descriptions of the various aspectsof the invention in conjunction with reference to the followingdrawings, where:

FIG. 1 is a block diagram depicting the components of a system formapping user behavior to brain regions of interest according to someembodiments of the present disclosure;

FIG. 2 is an illustration of a computer program product according tosome embodiments of the present disclosure;

FIG. 3 is an illustration of the creation of personalized models basedon sensed brain activity according to some embodiments of the presentdisclosure;

FIG. 4 is an illustration of functions of cognitive-behavioral andfunctional-anatomical models according to some embodiments of thepresent disclosure;

FIG. 5 is an illustration of multi-modal brain-state detection accordingto some embodiments of the present disclosure;

FIG. 6 is an illustration of targeted modulation of brain states viapersonalized cognitive and anatomical models according to someembodiments of the present disclosure;

FIG. 7 is an illustration of personalized functional-anatomical modelstargeting neurostimulations for optimal brain state induction accordingto some embodiments of the present disclosure;

FIG. 8 is an illustration of the targeted change of brain statesaccording to some embodiments of the present disclosure;

FIG. 9A is an illustration of optimal brain state induction according tosome embodiments of the present disclosure;

FIG. 9B is an illustration of prefrontal cortex activity duringbehavioral training according to prior art; and

FIG. 10 is an illustration of a human subject receiving neurostimulationaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The present invention relates to a system for mapping user behavior tobrain regions of interest and, more particularly, to a system formapping user behavior to brain regions of interest using a combinationof cognitive-behavioral and functional-anatomical modeling. Thefollowing description is presented to enable one of ordinary skill inthe art to make and use the invention and to incorporate it in thecontext of particular applications. Various modifications, as well as avariety of uses in different applications will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to a wide range of aspects. Thus, the present invention isnot intended to be limited to the aspects presented, but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

In the following detailed description, numerous specific details are setforth in order to provide a more thorough understanding of the presentinvention. However, it will be apparent to one skilled in the art thatthe present invention may be practiced without necessarily being limitedto these specific details. In other instances, well-known structures anddevices are shown in block diagram form, rather than in detail, in orderto avoid obscuring the present invention.

The reader's attention is directed to all papers and documents which arefiled concurrently with this specification and which are open to publicinspection with this specification, and the contents of all such papersand documents are incorporated herein by reference. All the featuresdisclosed in this specification, (including any accompanying claims,abstract, and drawings) may be replaced by alternative features servingthe same, equivalent or similar purpose, unless expressly statedotherwise. Thus, unless expressly stated otherwise, each featuredisclosed is one example only of a generic series of equivalent orsimilar features.

Furthermore, any element in a claim that does not explicitly state“means for” performing a specified function, or “step for” performing aspecific function, is not to be interpreted as a “means” or “step”clause as specified in 35 U.S.C. Section 112, Paragraph 6. Inparticular, the use of “step of” or “act of” in the claims herein is notintended to invoke the provisions of 35 U.S.C. 112, Paragraph 6.

Please note, if used, the labels left, right, front, back, top, bottom,forward, reverse, clockwise and counter-clockwise have been used forconvenience purposes only and are not intended to imply any particularfixed direction. Instead, they are used to reflect relative locationsand/or directions between various portions of an object. As such, as thepresent invention is changed, the above labels may change theirorientation.

Before describing the invention in detail, first a list of incorporatedliterature references as used in the description is provided. Next, adescription of various principal aspects of the present invention isprovided. Following that is an introduction that provides an overview ofthe present invention. Finally, specific details of the presentinvention are provided to give an understanding of the specific aspects.

(1) List of Incorporated Literature References

The following references are incorporated and cited throughout thisapplication. For clarity and convenience, the references are listedherein as a central resource for the reader. The following referencesare hereby incorporated by reference as though fully included herein.The references are cited in the application by referring to thecorresponding literature reference number, as follows:

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(2) Principal Aspects

The present invention has three “principal” aspects. The first is asystem for system for mapping user behavior to brain regions ofinterest. The system is typically in the form of a computer systemoperating software or in the form of a “hard-coded” instruction set.This system may be incorporated into a wide variety of devices thatprovide different functionalities. The second principal aspect is amethod, typically in the form of software, operated using a dataprocessing system (computer). The third principal aspect is a computerprogram product. The computer program product generally representscomputer-readable instructions stored on a non-transitorycomputer-readable medium such as an optical storage device, e.g., acompact disc (CD) or digital versatile disc (DVD), or a magnetic storagedevice such as a floppy disk or magnetic tape. Other, non-limitingexamples of computer-readable media include hard disks, read-only memory(ROM), and flash-type memories. These aspects will be described in moredetail below.

A block diagram depicting an example of a system (i.e., computer system100) of the present invention is provided in FIG. 1. The computer system100 is configured to perform calculations, processes, operations, and/orfunctions associated with a program or algorithm. In one aspect, certainprocesses and steps discussed herein are realized as a series ofinstructions (e.g., software program) that reside within computerreadable memory units and are executed by one or more processors of thecomputer system 100. When executed, the instructions cause the computersystem 100 to perform specific actions and exhibit specific behavior,such as described herein. The one or more processors may have anassociated memory with executable instructions encoded thereon such thatwhen executed, the one or more processors perform multiple operations.The associated memory is, for example, a non-transitory computerreadable medium.

The computer system 100 may include an address/data bus 102 that isconfigured to communicate information. Additionally, one or more dataprocessing units, such as a processor 104 (or processors), are coupledwith the address/data bus 102. The processor 104 is configured toprocess information and instructions. In an aspect, the processor 104 isa microprocessor. Alternatively, the processor 104 may be a differenttype of processor such as a parallel processor, or a field programmablegate array.

The computer system 100 is configured to utilize one or more datastorage units. The computer system 100 may include a volatile memoryunit 106 (e.g., random access memory (“RAM”), static RAM, dynamic RAM,etc.) coupled with the address/data bus 102, wherein a volatile memoryunit 106 is configured to store information and instructions for theprocessor 104. The computer system 100 further may include anon-volatile memory unit 108 (e.g., read-only memory (“ROM”),programmable ROM (“PROM”), erasable programmable ROM (“EPROM”),electrically erasable programmable ROM “EEPROM”), flash memory, etc.)coupled with the address/data bus 102, wherein the non-volatile memoryunit 108 is configured to store static information and instructions forthe processor 104. Alternatively, the computer system 100 may executeinstructions retrieved from an online data storage unit such as in“Cloud” computing. In an aspect, the computer system 100 also mayinclude one or more interfaces, such as an interface 110, coupled withthe address/data bus 102. The one or more interfaces are configured toenable the computer system 100 to interface with other electronicdevices and computer systems. The communication interfaces implementedby the one or more interfaces may include wireline (e.g., serial cables,modems, network adaptors, etc.) and/or wireless (e.g., wireless modems,wireless network adaptors, etc.) communication technology.

In one aspect, the computer system 100 may includes one or more of aninput device 112 coupled with the address/data bus 102, wherein theinput device 112 is configured to communicate information and commandselections to the processor 100. In accordance with one aspect, theinput device 112 includes an alphanumeric input device, such as akeyboard, that may include alphanumeric and/or function keys.Alternatively or in addition, the input device 112 may include an inputdevice other than an alphanumeric input device. For example, the inputdevice 112 may include one or more sensors, such as a camera for videoor still images, a microphone, or a neural sensor. Other example inputdevices 112 may include an accelerometer, a GPS sensor, or a gyroscope.

In an aspect, the computer system 100 may include a cursor controldevice 114 coupled with the address/data bus 102, wherein the cursorcontrol device 114 is configured to communicate user input informationand/or command selections to the processor 100. In an aspect, the cursorcontrol device 114 is implemented using a device such as a mouse, atrack-ball, a track-pad, an optical tracking device, or a touch screen.The foregoing notwithstanding, in an aspect, the cursor control device114 is directed and/or activated via input from the input device 112,such as in response to the use of special keys and key sequence commandsassociated with the input device 112. In an alternative aspect, thecursor control device 114 is configured to be directed or guided byvoice commands.

In an aspect, the computer system 100 further may include one or moreoptional computer usable data storage devices, such as a storage device116, coupled with the address/data bus 102. The storage device 116 isconfigured to store information and/or computer executable instructions.In one aspect, the storage device 116 is a storage device such as amagnetic or optical disk drive (e.g., hard disk drive (“HDD”), floppydiskette, compact disk read only memory (“CD-ROM”), digital versatiledisk (“DVD”)). Pursuant to one aspect, a display device 118 is coupledwith the address/data bus 102, wherein the display device 118 isconfigured to display video and/or graphics. In an aspect, the displaydevice 118 may include a cathode ray tube (“CRT”), liquid crystaldisplay (“LCD”), field emission display (“FED”), plasma display, or anyother display device suitable for displaying video and/or graphic imagesand alphanumeric characters recognizable to a user.

The computer system 100 presented herein is an example computingenvironment in accordance with an aspect. However, the non-limitingexample of the computer system 100 is not strictly limited to being acomputer system. For example, an aspect provides that the computersystem 100 represents a type of data processing analysis that may beused in accordance with various aspects described herein. Moreover,other computing systems may also be implemented. Indeed, the spirit andscope of the present technology is not limited to any single dataprocessing environment. Thus, in an aspect, one or more operations ofvarious aspects of the present technology are controlled or implementedusing computer-executable instructions, such as program modules, beingexecuted by a computer. In one implementation, such program modulesinclude routines, programs, objects, components and/or data structuresthat are configured to perform particular tasks or implement particularabstract data types. In addition, an aspect provides that one or moreaspects of the present technology are implemented by utilizing one ormore distributed computing environments, such as where tasks areperformed by remote processing devices that are linked through acommunications network, or such as where various program modules arelocated in both local and remote computer-storage media includingmemory-storage devices.

An illustrative diagram of a computer program product (i.e., storagedevice) embodying the present invention is depicted in FIG. 2. Thecomputer program product is depicted as floppy disk 200 or an opticaldisk 202 such as a CD or DVD. However, as mentioned previously, thecomputer program product generally represents computer-readableinstructions stored on any compatible non-transitory computer-readablemedium. The term “instructions” as used with respect to this inventiongenerally indicates a set of operations to be performed on a computer,and may represent pieces of a whole program or individual, separable,software modules. Non-limiting examples of “instruction” includecomputer program code (source or object code) and “hard-coded”electronics (i.e. computer operations coded into a computer chip). The“instruction” is stored on any non-transitory computer-readable medium,such as in the memory of a computer or on a floppy disk, a CD-ROM, and aflash drive. In either event, the instructions are encoded on anon-transitory computer-readable medium.

(3) Introduction

Neurostimulation is an activation of part of the nervous system usingelectrodes (or microelectrodes). Non-limiting examples ofneurostimulation include trans-cranial direct-current stimulation(tDCS), high definition (HD) tDCS, HD transcranial stimulation (HD-tCS),and transcranial magnetic stimulation. There is growing evidence thattrans-cranial neurostimulation interventions can enhance key cognitivefaculties associated with adaptive reasoning and problem solvingincluding creativity (see Literature Reference No. 44), visualperception (see Literature Reference No. 6), visuospatial attention (seeLiterature Reference No. 51), and working memory functions (seeLiterature Reference No. 24). Trans-cranial direct-current stimulation(tDCS) is believed to either enhance or suppress the activation ofneurons depending on the polarity of the electric field within theneural tissue (see Literature Reference No. 18). When this change inneural excitability is combined with endogenous neural activation duringa person's task-generated activities, neural firing patterns are alteredleading to both short-term and long-term changes in synaptic strengths.

Conventional two-electrode neurostimulations are impossible to focus ona targeted brain region, thus the resulting current flow throughapproximately half of the brain is not efficacious, especially whenconsidering extended sessions (see Literature Reference No. 16). SoterixMedical developed the first technology capable of non-invasive,low-intensity, targeted electrotherapy, called high-definitiontrans-cranial direct-current stimulation (HD-tDCS) (see LiteratureReference No. 62 for a description of HD-tDCS). This technology canincrease, suppress, or drive functional, localized activity in targetbrain areas with a degree of precision and multi-regionalparallelization never attainable using the standard two electrode tDCS.

Unlike deep brain stimulation and transcranial magnetic stimulation(TMS), HD-tDCS is designed to be portable and can be used at a clinic orat home with no pain, significant side effects, or risk of injury.Currents can be guided through the brain in an application- andsubject-specific manner (see Literature Reference Nos. 10, 17, and 57).In comparison to conventional tDCS, HD-tDCS produces larger andlonger-lasting effects in brain neuroplasticity (see LiteratureReference Nos. 35 and 49). Despite the benefits, this technology hasnever been integrated with real-time, closed-loop, multi-modal sensingfor directing neurostimulation, nor has HD-tDCS been directed throughpersonalized and adaptive models.

In the system according to some embodiments of the present disclosure, acombination of cognitive-behavioral and functional-anatomical modelingare employed to provide comprehensive mapping from user behavior tobrain regions of interest for neurostimulation patterns as described infurther detail below.

Non-model-driven neurostimulation methods have been shown to enhancecognitive faculties such as inhibitory control (anodal PFC (prefrontalcortex) stimulation increasing activity (see Literature Reference No.101)), working memory-anodal left-dlPFC (dorsolateral PFC (seeLiterature Reference No. 9), planning ability-(cathodal/anodal dlPFC(see Literature Reference No. 21)), task shifting (anodal dlPFC & M1)(see Literature Reference No. 39), feature categorization and cognitivecontrol (cathodal left (see Literature Reference No. 41)), insight(anterior temporal lobe) (see Literature Reference No. 13), anddiminished cognitive control (Left-PFC) (see Literature Reference No.14). Although providing groundwork for functional assignments, andshowing promising results, these singular neurostimulation studies havenot tested enhancement effects across multiple brain regions, networks,dynamic activities or measured enhancement generalization or duration.

Additionally, previous works have not personalized stimulation protocolsthrough cognitive-behavioral and functional-anatomical models for peakperformance and maximal benefit. Further, the overall effectiveness oftreatment has been limited by a lack of personalization and real-timebrain state-driven closed-loop feedback. Those efforts to personalizeneurostimulation from computational models have been limited to purelyanatomical models (see Literature Reference Nos. 2 and 3).

(4) Specific Details of the Invention

The system according to some embodiments of the present disclosurecombines revolutionary work in high-definition (HD)-neurostimulationwith personalized model-driven behavioral training to induce peakperformance and lasting changes to the underlying neural systems,causing increased abilities in a variety of possible areas, including,but not limited to adaptive reasoning, problem solving, memoryenhancement, rehabilitation after traumatic brain injury, improvedexecutive function and motor performance.

With current state-of-the-art trans-cranial direct-current stimulation(tDCS), enhancement effects show promise but vary drastically acrosssubjects, some even declining in performance. The present inventionsolves this problem by using a personalized method that will improve thedesired neural functions using multiple innovations as follows. Asdepicted in FIG. 3, users will perform behavioral tasks 300 while brain302 activity is sensed by neuroimaging, non-limiting examples of whichinclude fNIRS (functional near-infrared spectroscopy), EEG(electroencephalogram), and/or functional magnetic resonance imaging304. Coupled cognitive-behavioral models 306 and functional-anatomicalmodels 308 prescribe behavioral tasks and patterns of neurostimulation(via a task generator 310) to modify brain 302 region activity toimprove performance in the desired region, and HD-neurostimulation(i.e., HD-tDCS stimulation 312) precisely targets those regions.Transcranial direct current stimulation (tDCS) functions by sendingconstant, low direct current through electrodes attached with the headof a human subject. When these electrodes are placed in the region ofinterest, the current induces intracerebral current flow. This currentflow then either increases or decreases the neuronal excitability in thespecific area being stimulated based on which type of stimulation isbeing used.

During training, EEG and fNIRS 304 provide feedback on a user's responseto stimulation (i.e., HD-tDCS stimulation 312), while the coupled models(the cognitive-behavioral 306 and functional-anatomical 308 models)adapt to reflect individual differences, performance gains, andpersonalize training for peak performance for each individual. Thesecausal models connect performance with explanatory and predictivemechanisms. Users will continue to use the system until the individualpeak performance is obtained.

(4.1) Concept of Operation

The basic concept of operation, shown in FIG. 4, is a multi-step processof choosing the correct behavioral task from a set of task options 400,stimulating the correct brain areas (i.e., task with stimulation 402),measuring the changing brain states 404, and modifying the personalizedmodel (cognitive-behavior model 306 and functional-anatomical model308). The correct brain areas are selected using thecognitive-behavioral model 306.

Users first must determine the type of cognitive improvement they desire(i.e., desired brain state 406). Non-limiting examples of cognitiveimprovement include increased working memory capacity, increased motorreaction times, and increased decision making performance. Once this hasbeen chosen the cognitive-behavioral model 306 will determine what task(i.e., chosen task 408) would be best suited for such improvement. Anon-limiting example of a chosen task 408 is aircraft control learning(i.e., learning to fly a plane). The functional-anatomical model 308then determines what stimulation protocol to use for the given task(i.e., task with stimulation 402). For example, for working memoryimprovement, anodal stimulation of the right dlPFC can be used, asdescribed in Literature Reference No. 64. The first time stimulationoccurs the stimulation will be a general task specific stimulation andthe models will adapt to the personal behavioral responses to the task.As a non-limiting example of a general task specific stimulation,working memory (a general cognitive faculty) can be trained with anN-back task and dlPFC stimulation. As the user performs tasks (i.e.,task with stimulation 402) the brain state is measured (i.e., measuredbrain state 404) by multiple neural imaging modalities (fNIRS, EEGand/or fMRI (functional magnetic resonance imaging)). Thecognitive-behavior model 306 will look for changes in brain states(i.e., measured brain state 404) as the behavioral tasks (i.e., taskwith stimulation 402) are performed by the user, suggesting new tasks aswell as identifying regions of interest to be stimulated in task withstimulation 402 that are personalized for the current user based ontheir previous brain states (i.e., measured brain state 404).

The process according to some embodiments of the present disclosurecomprises task selection (i.e., chosen task 408), brain areaidentification using the cognitive-behavioral model 306, a stimulationprotocol (i.e., task with stimulation 402), analysis of task performance410 (e.g., percentage of correct responses), and brain state measurementand analysis (i.e., measured brain state 404). The process repeats asthe user improves in their desired area of cognitive enhancement.

(4.2) Multi-Modal Sensor Integration

The adaptive stimulation approach according to some embodiments of thepresent disclosure incorporates two modes of brain monitoring tofacilitate feedback as the intervention takes place. The first is to useEEG to monitor dynamic brainwave power in the alpha and gamma frequencybands over the anterior cingulate region. This should indicate whether asubject is already predominately in an analytic or insight mode, asdescribed in Literature Reference No. 32. The second is to use fNIRS tomonitor activity in the prefrontal network. fNIRS monitoring of theprefrontal cortex has been shown to provide a good indication ofcognitive workload (see Literature Reference No. 8).

Unique advances have been developed to allow both EEG and fNIRS data tobe collected during the stimulation intervention. For example, HD-tCS(high definition trans-cranial stimulation) electrodes are the same formfactor as EEG electrodes and can be placed on the same head cap (orheadband), as shown in FIG. 8. Interference between electricalstimulation currents and EEG is avoided by alternating the timing of thestimulation and EEG data capture switching up to 250 Hertz (Hz). Thisensures sufficient EEG data can be collected below the Nyquist limit of125 Hz while alternating current (AC) stimulation is provided. In thisway, one can collect snippets of EEG data throughout the stimulationperiod without interference. Although any current stimulation producesincreased blood flow on the scalp that can interfere with the fNIRSinfrared signal, there are ways to process the fNIRS data to minimizesources of noise so that it can be used in conjunction with tCS (seeLiterature Reference Nos. 1, 26, 27, and 31 for descriptions ofprocessing fNIRS data).

FIG. 5 depicts multi-modal brain-state detection. Targeted change ofbrain states is achieved by simultaneous excitation from task-generatedactivity while providing stimulation current. Anatomical personalization500 occurs via functional magnetic resonance imaging (fMRI) anddiffusion tensor imaging (DTI) mapping of individual's brains (fMRI/DTI502). The three-dimensional (3D) volumes of the individual's brainstructure and connectivity are used to calibrate the fNIRS sensinglocations (i.e., fNIRS calibration 504) for real-time functionalpersonalization 506. The EEG and real-time biofeedback system (EEGrt-Biofeedback 508 can be used in tandem to personalize stimulations(i.e., activity personalization 510) for detected functional frequenciesin the ROIs derived from the fMRI/DTI 502. Additionally, fNIRStechniques 512 can be used for EEG calibration 514.

(4.3) Neurostimulation Parameter Space

Described herein is the use of HD-tDCS to drive individualizedspatiotemporal stimulation pattern “montages” of regional activities(direct current (DC) stimulation) and complex region dynamics(alternating current (AC) stimulation) to activate or suppress specifictarget brain regions, networks, and dynamic states to optimizebehavioral performance. By personalizing these stimulations though acombination of individualized cognitive-behavioral 306 and functionalanatomical models 308, one can reduce the high variability seen withstandard tDCS and ensure that each user is trained to engage the mostoptimal brain states and behavioral strategies (see Literature ReferenceNo. 56).

Recent work has shown the importance of interactions between brainregions. The prefrontal cortex, for example, can act as a top-downfilter to reduce distractions from bottom-up sensory inputs (seeLiterature Reference No. 46). At certain times, suppression of thisfiltering action may be beneficial to creative problem solving (e.g., byallowing competing hypotheses to be entertained). Modulatinginteractions between multiple brain regions requires the ability tosimultaneously target numerous modal regions across the brain usingregion-specific stimulation protocols, which the system according tosome embodiments of the present disclosure provides. This ability,combined with the model-driven stimulus generation according to someembodiments of the present disclosure, provides the needed flexibilityto enhance the diverse set of cognitive faculties involved in solvingproblems in information-rich environments in high performing adults.

The HD-tCS system according to some embodiments of the presentdisclosure will support 9 DC/AC stimulation channels and 32 EEG channelsfor concurrent data collection (interleaved up to 250 Hz). Thestimulation channels will support any combination of three differenttypes of HD-tCS. Direct Current Stimulation (HD-tDCS) is used to inhibitor excite targeted functional brain regions. Alternating CurrentStimulation (HD-tACS) induces oscillatory patterns of neural activitywith target amplitudes, frequencies, and phases. Random NoiseStimulation (HD-tRNS) will promote neural plasticity (see LiteratureReference Nos. 19, 42, 48, and 49).

(4.4) Model-Driven Stimulus Generation

Reasoning strategies and brain states may vary from one person to thenext during problem solving, and HD-tCS has the ability to preciselyapply neurostimulation with high resolution to induce personalized brainstates. A generic neurostimulation is inappropriate; therefore, thepresent invention is model-based, and the models adapt during trainingfrom subject-specific fMRI, fNIRS, and EEG data. Thecognitive-behavioral model 306 and the functional anatomical model 308,account for individual differences from a cognitive and an anatomicalperspective, respectively, and determine a combination of electrodecurrents to produce on a subject's scalp so as to modulate target brainregions.

FIG. 6 illustrates personalized cognitive and anatomical models directthe simultaneous production of HD-neurostimulations and behavioraltraining tasks to enable targeted modulation of specific brain regions,networks, and dynamic states. The first stage of modeling assessesbehavioral performance deficiencies in healthy high-performing adults(having a user profile 600) and associates them with activation statesin various brain regions. Non-limiting examples of behavioralperformance deficiencies include lower working memory capacity,decreased set-shifting abilities, and cognitive biases that decrease thedesired task performance (element 410). To initialize a phenotypiccognitive-behavioral model 306, the subject is engaged in a battery ofcognitive tasks while being scanned with Mill, and the performance isused to parameterize the cognitive-behavioral model 306. LiteratureReference Nos. 12 and 24 describe non-model examples of behavioral tasksthat could inform the cognitive-behavioral model of the presentinvention. This model will be implemented in a cognitive simulator (suchas ACT-R described in Literature Reference No. 12). Once parameterized,the model predicts the subject's performance over the full spectrum ofrelated tasks (see Literature Reference No. 11). Using identifiedweaknesses (e.g., limited working memory capacity, slow reaction times),the cognitive-behavioral model 306 will then be used to assemble a setof training tasks (task options 400 in FIG. 4), and for each task itwill search for the desired “target” brain states (element 406), whichis the state measured during peak behavioral performance duringpersonalization that will yield the greatest estimated improvement inoverall performance on the new task.

The second stage of modeling selects one of the training tasks at a time(element 408) to present to the subject, along with its associatedtarget brain state (element 406). Three-dimensional (3D)functional-anatomical models 308, such as the Virtual Brain described inLiterature Reference No. 40, capture both shape and conductance oftissues above and beneath the skull. Functional-anatomical models 308associate functional brain regions for the target brain state (element406) to specific physical locations within the skull and provide a meansto determine the HD-tCS electrode currents needed to reach these regionseffectively. This functional-anatomical model 308 must be initializedfrom a user's fMRI scan, but during the training regimen it is adaptedbased on fNIRS sensing, which provides a lower-cost rough approximationto fMRI data.

Given a set of brain regions designated for intervention and the desiredactivations of these regions, the functional-anatomical model 308 canderive the needed electrode currents and polarities to induce the targetbrain state (element 406). The stimulation is applied while the subjectis engaged in the selected task (element 408). The method according tosome embodiments of the present disclosure utilizes three primarycategories of behavioral training and assessment tasks. The firstcategory is a task from primary reasoning and problem solving, such asdescribed in Literature Reference No. 24. The second category consistsof tasks involving executive functions and the efficiency of switchingbetween insight and analytical problem-solving strategies, such asdescribed in Literature Reference Nos. 13 and 32. The third categoryinvolves reasoning about information rich environments presented innarratives about a complex topic, such as social norms of differentcultures. However, the method according to some embodiments of thepresent disclosure does not depend on these specific tasks and isgeneral to any form of cognitive or behavioral faculties.

Finally, the HD-tCS currents guide the subjects' neural activity duringthe tasks into states that assist subjects in realizing peak behavioralperformance. Peak behavioral performance is assessed relative to expertsin the field (e.g., pilots), and novice to expert transitions (e.g.,learning to fly an airplane) are measured. Furthermore, these targetbrain states promote neural plasticity essential for improvement andpersistence, while also enhancing the generalizability and retention ofthe cognitive skills developed during the training. Literature ReferenceNos. 23, 25, and 37 postulate neural plasticity as the mechanism ofaction and demonstrate behavioral enhancement effects.

(4.5) Stimulus Adaptation and Neurofeedback

The third element of the method according to some embodiments of thepresent disclosure is to dynamically alter the stimulus currents basedon sensor feedback of a subject's brain states both before and duringengagement in behavioral tasks. The system described herein manipulatesthe oscillatory dynamics present in neural activity of specific brainregions in order to train and assist users in flexibly switching betweenmodes of problem solving. The present invention functions byincorporating Alternating Current stimulation (HD-tACS), at gamma-bandfrequencies (˜40 Hz) for activation and alpha-band frequencies (˜10 Hz)for suppression, into a feedback loop that involves real-time sensingfrom both EEG and fNIRS. For the first time, data from both modalitiesis used during the course of transcranial stimulation.

FIG. 7 depicts personalized functional-anatomical models targetingHD-neurostimulations for optimal brain state induction. As describedabove, HD-tDCS is used to drive individualized spatiotemporalstimulation pattern “montages” (i.e., HD-tDCS/tACS montage 700) ofregional activities (DC stimulation) and complex region dynamics (ACstimulation) to activate or suppress specific target brain regions,networks, and dynamic states to optimize behavioral performance.Functional-anatomical personalization 702 (using thefunctional-anatomical models 308) attempts to have a predicted brainstate 704 match the desired brain state 406 by determining the HD-tCSelectrode currents (or HD-tDCS/tACS montage 700) needed to reach thedesired brain regions effectively. If the predicted brain state 704 doesnot match the desired brain state 406, then the HD-tDCS/tACS montage 700is adjusted by the functional-anatomical models 308 until a match isrealized.

In order to solve complex real-world problems, individuals need to beadaptive and use a combination of problem-solving strategies (seeLiterature Reference Nos. 32 and 52). However, most people tend to havea natural predilection toward using one strategy or the other (seeLiterature Reference No. 33), and they have difficulty switching betweenthem. Recent neuroimaging research has identified differences in brainstates associated with analysis and insight (see Literature ReferenceNo. 32). For example, insight solving involves a burst of activity inthe right temporal lobe (see Literature Reference No. 29). Immediatelyprior to the presentation of an expected problem, subsequent insightsolving is associated with elevated activity in the anterior cingulateand bilateral temporal lobes (see Literature Reference No. 34).

The real-time, closed-loop, multi-modal sensing of the system accordingto some embodiments of the present disclosure informs thecognitive-behavioral 306 and functional-anatomical 308 models forguided, adaptive and personalized neurostimulation to steer users towardthese desired brain states 406. There are two key benefits of thiscapability. First, it increases the efficiency and efficacy oftraditional neurostimulation and neurofeedback training, in which asubject will learn to “mentally steer” his or her brain state towardsone of two target states. Second, it allows the induction of the desiredtarget brain states 406 while a user is actively engaged in behavior, acapability that is infeasible during traditional neurofeedback training.

The method of adaptive stimulation according to some embodiments of thepresent disclosure facilitates a more flexible switching between modesof problem solving. For example, the method can induce the analyticbrain state in subjects by stimulating their anterior cingulate regionwith alpha frequencies to decrease activity (see Literature ReferenceNo. 50). This reduces the brain's monitoring of competing solutionpossibilities, resulting in a focused analytic strategy that follows thedominant, obvious path to solution (see Literature Reference Nos. 34 and55). As cognitive workload increases, presumably because the subject is“stuck” (i.e., does not increase their skill level given additionaltraining with respect to other subjects on this task) and can't makefurther progress, the system guides the user/subject to enter theinsight brain state and then resume the task. This involves thecognitive-behavioral 306 and functional-anatomical 308 modelsstimulating the anterior cingulate region with gamma frequencies toincrease activity. This will sensitize users to competing, nonobvioussolution possibilities—“long shot” ideas. When the anterior cingulatedetects weak, unconscious ideas, it can signal the dorsolateral PFC toswitch to an idea (see Literature Reference No. 45) resulting in aninsight (see Literature Reference No. 29).

(4.6) Model-Driven Neurostimulation Parameter Selection

(4.6.1) Target and Predicted Brain States

The cognitive-behavioral models 306 predict both level and flow ofactivation within and between regions of interest in the brain that arenecessary to maximize learning and task performance. These regions andspread of activity will be personalized based on prior task performancealong with previous model brain state predictions. The output will theninform the functional-anatomical model 308 to create patterns ofHD-neurostimulation (element 402) appropriate to reach these brainregions at the target levels of activation.

The adaptivity of the model between training sessions is essential asthe model must recognize performance that is not in line with itsprediction (i.e., predicted state 704), determine the cause of themismatch, and then adapt its training regimen to the new conditions. Ifa subject's performance worsens (as some do in non-personalized tDCS),the model adapts the task set given to the subjects or recommends newregional activation patterns until that individual improves. Juvina etal. used an ACT-R model to identify the benefit of early tDCS in atarget search task, while also identifying the benefit of late tDCS in achange detection task (see Literature Reference No. 30). The model fitthe differential influence and enhancement effect of both early and latetDCS and was compared to a control group which received no tDCS. Thisadaptation must also occur if the subject outperforms expectations, taskperformance increases faster than expected, and/or the task dependentbrain activation patterns change.

(4.6.2) Model Personalization

As described above, personalization consists of two main approaches:setting architectural parameters (see Literature Reference No. 15 for adescription of architectural parameters) and defining knowledge andskill structures (see Literature Reference No. 58 for a description ofknowledge and skill structures). In the first approach, cognitivecapacities are estimated from diagnostic tests and are then mapped ontoarchitectural parameters. These parameters are then applied to the modelto predict an individual's task performance and determine which tasks(and stimulations) will show the most generalized improvements. Thelatter approach to personalization will be to estimate the state of anindividual's knowledge from the subject's performance (intelligenttutoring) and to determine which knowledge structures the participanthas available and which new structures (e.g., skills) would maximize theparticipant's task performance.

FIG. 8 depicts achieving targeted change of brain states of a subject800 by simultaneous excitation from task-generated activity (i.e.,personalized task stimulus 802) while providing stimulation current(i.e., personalized neurostimulation 804) through electrodes 806positioned in a cap 808 worn by the user 800. Electrical activity of thebrain is recorded along the scalp of the user 800 via EEG 810.Additionally, the cap 808 includes an illumination source 812 and fNIRSdetectors 814 (or detection sensors). fNIRS measures blood flow changesin the brain using near infrared light (emitted from the illuminationsource 812), which can pass into the brain to detect (with the fNIRSdetectors 814) changes in blood oxygenation that can affect brainfunction and physiology. The set of fNIRS and EEG recordings 816 arethen used to inform the functional-anatomical model 308 to createpatterns of HD-tCS stimulation 818 in the user 800 via the cap 808 aspart of the personalized neurostimulation 804. With HD-tCS, multiplesmaller sized gel electrodes can be used to target specific corticalstructures. While the description of a cap 808 is used as the means forproviding stimulation and sensing electrical activity, any device thatprovides suitable contact with the scalp of the subject, such as aheadband, may be utilized.

FIGS. 9A and 9B illustrate phenotypic personalization and expertisetraining according to some embodiments of the present disclosure. FIG.9A depicts how the personalized adaptive method according to someembodiments of the present disclosure results in improved brain stateinduction for phenotypic subject categories. Initial brain states 900undergo an intervention stimulus 902 to reach an optimal target brainstate 904. As non-limiting examples, the phenotypic subject categoriesof an optimal target brain state 904 include a creative thinking group906 and an analytic thinking group 908.

FIG. 9B illustrates fNIRS pilot data showing that prefrontal cortexactivity (indicative of mental effort on task) generally reduces overthe course of 9 days of behavioral training, after an initial increase.This is interpreted as increasing efficiency with expertise (seeLiterature Reference No. 8). Prefrontal cortex activity is measured intotal hemoglobin (Hb) changes.

FIG. 10 illustrates a human subject 800 receiving neurostimulationaccording to some embodiments of the present disclosure. A device 1000able to generate an electrical current delivers neurostimulation byapplying a current through one electrode 806 (e.g., anode), and it flowsthrough the brain to another electrode 806 (e.g., cathode).

What is claimed is:
 1. A computer-implemented method for mapping user behavior to brain regions of interest, the computer-implemented method using one or more processors to perform operations of: selecting, using a functional-anatomical model coupled to a cognitive-behavior model, a set of high-definition neurostimulations, wherein the selected set of high-definition neurostimulations targets specific regions of a user's brain; sensing conditions in the user's brain state during application of the set of high-definition neurostimulations and performance of a selected behavioral task using at least one brain monitoring technique; adapting the coupled functional-anatomical and cognitive-behavior models until the desired brain state is reached; associating, using the functional-anatomical model, brain regions of the user for the desired brain state to specific physical locations within the skull of the user; selecting, using the functional-anatomical model, the set of high-definition neurostimulations to be applied to reach the associated brain regions of the user effectively to induce the desired brain state; assessing a set of behavioral performance deficiencies in the user; associating the set of behavioral performance deficiencies with brain states in various brain regions of the user; analyzing the user with a neuroimaging device as the user performs a plurality of behavioral tasks, wherein the user's performance is used to parameterize the cognitive-behavior model; implementing the cognitive-behavior model in a cognitive simulator; using the cognitive-behavior model to predict the user's performance for a plurality of related behavioral tasks; using the cognitive-behavior model to generate the set of behavioral tasks; and for each task, searching the set of behavioral tasks for the desired brain state for the user.
 2. The method as set forth in claim 1, wherein the one or more processors further perform operations of: assessing, using the cognitive-behavior model, changes in the user's brain state as the user performs the selected behavioral task; and outputting a new behavioral task in the set of behavioral tasks for the user to perform.
 3. The method as set forth in claim 2, wherein the one or more processors further perform an operation of identifying, using the cognitive-behavior model, specific regions of the user's brain to be targeted with a selected set of high-definition neurostimulations during performance of the new behavioral task based on a previous brain state of the user.
 4. The method as set forth in claim 1, wherein two brain monitoring techniques are used to sense changes in the user's brain state, wherein the first brain monitoring technique is electoencephalography (EEG) to monitor brain activity in an anterior cingulate region of the user's brain, and wherein the second brain monitoring technique is functional near-infrared spectroscopy (fNIRS) to monitor brain activity in a prefrontal cortex region of the user's brain. 